Non-cystic fibrosis bronchiectasis has been the subject of renewed interest over recent years. It is usually part of the evolutionary process of many infectious, autoimmune, genetic, developmental or allergic diseases. Its presentation and prognosis are heterogeneous and it causes significant morbidity and mortality with a real impact on the health care system. Thanks to increasingly available guidelines, it is now possible to define the optimal management that will include various therapeutic objectives : airway clearance, prevention and eradication of bacterial colonization, reduction of airway inflammation and exacerbations and improvement of quality of life.
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Antifungal-drug interactions in oncology: A cross-sectional study highlighting the role of pharmacists.
J Oncol Pharm Pract
January 2025
Department of Pharmacy, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan.
Study Objective: Complex pharmacotherapy in cancer patients increases the likelihood of drug-drug interactions (DDIs). Pharmacists play a critical role in the identification and management of DDIs. The aim of present study was to evaluate the role of pharmacist in identifying antifungal drug interactions in cancer patients and providing relevant recommendations.
View Article and Find Full Text PDFBarriers to healthcare professionals screening, recognizing, and managing delirium in the adult patients receiving specialist palliative care: a mixed-methods systematic review.
BMC Palliat Care
January 2025
Department of Nursing, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, No. 599 Dayang East Road, Linhai, Zhejiang, 317000, China.
Background: Delirium frequently occurs in palliative care settings, yet its screening, identification, and management remain suboptimal in clinical practice. This review aims to elucidate the barriers preventing healthcare professionals from effectively screening, recognizing, and managing delirium in adult patients receiving specialist palliative care, with the goal of developing strategies to enhance clinical practice.
Methods: A mixed-methods systematic review was conducted (PROSPERO: CRD42024563666).
Comparison of the sociodemographic and clinical profiles of cancer patients admitted to a tertiary palliative care unit before and during the COVID-19 pandemic.
BMC Palliat Care
January 2025
Palliative Care Unit, National Cancer Institute, Rio de Janeiro, Brazil.
Objective: To compare the sociodemographic and clinical profiles of patients with advanced cancer admitted to a tertiary palliative care unit before and during the COVID-19 pandemic.
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Modeling and simulation of distribution and drug resistance of major pathogens in patients with respiratory system infections.
BMC Infect Dis
January 2025
Department of Respiratory Medicine, Anting Hospital of Jiading District, 1060 Hejing Road, Anting Town, Jiading District, Shanghai, 201805, China.
Background: Respiratory tract infections (RTIs) are one of the leading causes of morbidity and mortality worldwide. The increase in antimicrobial resistance in respiratory pathogens poses a major challenge to the effective management of these infections.
Objective: To investigate the distribution of major pathogens of RTIs and their antimicrobial resistance patterns in a tertiary care hospital and to develop a mathematical model to explore the relationship between pathogen distribution and antimicrobial resistance.
AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios.
BMC Pregnancy Childbirth
January 2025
Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of Utah Health, 30 N. Mario Capecchi Dr., Level 5 South, Salt Lake City, UT, 84132, USA.
Background: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR.
Methods: Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.
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